genieclust (version 0.9.4)

gini_index: Inequity (Inequality) Measures

Description

gini_index() gives the normalised Gini index and bonferroni_index() implements the Bonferroni index.

Usage

gini_index(x)
bonferroni_index(x)

Arguments

x

numeric vector of non-negative values

Value

The value of the inequity index, a number in \([0, 1]\).

Details

Both indices can be used to quantify the "inequity" of a numeric sample. They can be perceived as measures of data dispersion. For constant vectors (perfect equity), the indices yield values of 0. Vectors with all elements but one equal to 0 (perfect inequity), are assigned scores of 1. Both indices follow the Pigou-Dalton principle (are Schur-convex): setting \(x_i = x_i - h\) and \(x_j = x_j + h\) with \(h > 0\) and \(x_i - h \geq x_j + h\) (taking from the "rich" and giving to the "poor") decreases the inequity.

These indices have applications in economics, amongst others. The Gini clustering algorithm uses the Gini index as a measure of the inequality of cluster sizes.

The normalised Gini index is given by: $$ G(x_1,\dots,x_n) = \frac{ \sum_{i=1}^{n-1} \sum_{j=i+1}^n |x_i-x_j| }{ (n-1) \sum_{i=1}^n x_i }. $$

The normalised Bonferroni index is given by: $$ B(x_1,\dots,x_n) = \frac{ \sum_{i=1}^{n} (n-\sum_{j=1}^i \frac{n}{n-j+1}) x_{\sigma(n-i+1)} }{ (n-1) \sum_{i=1}^n x_i }. $$

Time complexity: \(O(n)\) for sorted (increasingly) data. Otherwise, the vector will be sorted.

In particular, for ordered inputs, it holds: $$ G(x_1,\dots,x_n) = \frac{ \sum_{i=1}^{n} (n-2i+1) x_{\sigma(n-i+1)} }{ (n-1) \sum_{i=1}^n x_i }, $$ where \(\sigma\) is an ordering permutation of \((x_1,\dots,x_n)\).

References

Bonferroni C., Elementi di Statistica Generale, Libreria Seber, Firenze, 1930.

Gagolewski M., Bartoszuk M., Cena A., Genie: A new, fast, and outlier-resistant hierarchical clustering algorithm, Information Sciences 363, 2016, pp. 8-23. doi:10.1016/j.ins.2016.05.003

Gini C., Variabilita e Mutabilita, Tipografia di Paolo Cuppini, Bologna, 1912.

Examples

# NOT RUN {
gini_index(c(2, 2, 2, 2, 2))  # no inequality
gini_index(c(0, 0, 10, 0, 0)) # one has it all
gini_index(c(7, 0, 3, 0, 0))  # give to the poor, take away from the rich
gini_index(c(6, 0, 3, 1, 0))  # (a.k.a. Pigou-Dalton principle)
bonferroni_index(c(2, 2, 2, 2, 2))
bonferroni_index(c(0, 0, 10, 0, 0))
bonferroni_index(c(7, 0, 3, 0, 0))
bonferroni_index(c(6, 0, 3, 1, 0))

# }